PCA and PLS aims:to get some
insight into the bilinear factor models Principal Component Analysis
(PCA) and Partial Least Squares (PLS) regression, focusing on the
mathematics and numerical aspects rather than how s and why s of
data analysis practice. For the latter part it is assumed (but not
absolutely necessary) that the reader is already familiar with these
methods. It also assumes you have had some preliminary experience
with linear/matrix algebra.
A Tutorial on Principal Component Analysis.Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that
is widely used but poorly understood. The goal of this paper is to dispel the magic behind this
black box.
Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the Principal
％ component subspace U of dimension PPCA_DIM using a centred covariance
matrix X. The variable VAR contains the off-subspace variance (which
is assumed to be spherical), while the vector LAMBDA contains the
variances of each of the Principal components. This is computed
using the eigenvalue and eigenvector decomposition of X.
pca人臉識別This package implements basic Principal Component Analysis in Matlab and
tests is with grayscale portion of the FERET database. Images are not
preprocessed and it is up to the user to preprocess the images as wanted,
not changing the filenames
PCA人臉識別
This package implements basic Principal Component Analysis in Matlab and
tests is with grayscale portion of the FERET database. Images are not
preprocessed and it is up to the user to preprocess the images as wanted,
not changing the filenames
PCA人臉識別 This package implements basic Principal Component Analysis in Matlab and
tests is with grayscale portion of the FERET database. Images are not
preprocessed and it is up to the user to preprocess the images as wanted,
not changing the filenames
PCA人臉識別 This package implements basic Principal Component Analysis in Matlab and
tests is with grayscale portion of the FERET database. Images are not
preprocessed and it is up to the user to preprocess the images as wanted,
not changing the filenames
PCA人臉識別 This package implements basic Principal Component Analysis in Matlab and
tests is with grayscale portion of the FERET database. Images are not
preprocessed and it is up to the user to preprocess the images as wanted,
not changing the filenames
